look at the time(line)

It would benefit us to see a timeline of when each monument was dedicated by state. Awhile ago there was this thread on Twitter that discussed this visualization from the New York Times around the cost of national disasters. This visualization is one of the best examples of conveying a large amount of information in a very aesthetically pleasing way. I, along with others it seems, spent a bit of time trying to come up with other ways to represent this data and I certainly could not match the original. So calling to that New York Times chart as inspiration we can try and plot the timeline of confederate dedications for the state of Virigina.

va<-subset(confederate.na,state=='VA')

ggplot(va,aes(x=year_dedicated,y=reorder(state,counts),color=group_class))+
  geom_count(alpha=.35,show.legend = F)+
  a_theme(emphasis='x')+
  a_scale_color()+
  scale_x_continuous()+
  labs(x='Year',
       y='State')

This gives us a detailed look into the spread of thsoe dedications over time, so now we just want to see how every state looks on the same timeline.

ggplot(confederate.na,aes(x=year_dedicated,y=reorder(state,counts),color=group_class))+
  geom_count(alpha=.35)+
  a_theme(emphasis='x')+
  theme(legend.position="top")+
  scale_x_continuous(position=c('top'))+
  labs(x='Year',
       y='State',
       title='When confederate monuments were dedicated',
       subtitle='A timeline of dedicated Confederate monuments by State with the most recent being dedicated in VA in 2014.\nMonuments with unknown years of dedication are omitted.',
       caption='Data: Southern Poverty Law Center')

a closer look

With this sort of visualization we can see some high-level trends. Like the big band of monuments dedicated in the early 1900’s. Then a higher concentration of schools dedicated just after 1950. To dive specifically into the types of different dedications it could be useful to look specifically at each category. Using the statebins package authored by Bob Rudis allows us to look at the distribution of specific categories across the states with a few lines.

states<-data.frame(state=state.abb,stringsAsFactors = F)
all.mon<-left_join(states,confederate)
all.mon[is.na(all.mon)]<-0
all.mon%>%
  group_by(state)%>%
  summarize(count=n()) %>%
  select(state=state, value=count) %>%
  statebins_continuous(legend_position="bottom", font_size=3, text_color='#ffffff',legend_title='count')+
  labs(subtitle=nrow(all.mon))+
  a_theme(grid=F,noaxis=T)